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Record W1909410985 · doi:10.1002/jclp.21947

Nonsuicidal Self-Injury, Coping Strategies, and Sexual Orientation

2013· article· en· W1909410985 on OpenAlexaff
Michael J. Sornberger, Nathan Grant Smith, Jessica R. Toste, Nancy L. Heath

Bibliographic record

VenueJournal of Clinical Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsSexual orientationPsychologyCoping (psychology)Maladaptive copingClinical psychologyLesbianMultivariate analysis of varianceLogistic regressionDevelopmental psychologyPoison controlSocial psychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: The current study sought to investigate the relationship between sexual orientation and nonsuicidal self-injury (NSSI). This study also includes an examination of coping styles, both maladaptive and adaptive, based on sexual orientation. METHOD: Participants included 207 young adults who identified as lesbian/gay, bisexual, or questioning (50.2% female) and a heterosexual comparison group. RESULTS: A hierarchical logistic regression showed that bisexual and questioning individuals were more likely to report having engaged in NSSI in their lifetime. A chi-square yielded no difference between groups on frequency of NSSI. Multivariate analyses of variance examining maladaptive and adaptive coping strategies demonstrated that bisexual and questioning individuals reported greater use of maladaptive strategies than the heterosexual group; however, there was little difference between groups on adaptive coping. CONCLUSIONS: The relationship between sexual orientation and coping appears to be a complex one, suggesting that bisexual and questioning individuals attempt to use a wide range of coping mechanisms, possibly due to increased stress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.145
GPT teacher head0.504
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations66
Published2013
Admission routes1
Has abstractyes

Explore more

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